Artificial Intelligence To Reduce Radiation-induced Cardiotoxicity ...

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AI to Reduce Radiation-induced Cardiotoxicity in Lung Cancer Radiotherapy:
A Novel Functional Radiomics
using Cardiac FDG-...

• Assistant Professor / Computational Medical Physicist
Sidney Kimmel Medical College / Cancer Center at Thomas Jefferson ...

• Introduction
Radiation Oncology
Medical Physics
Radiomics
• Heart Toxicity Prediction
Method
Results
• Conclusion
3...

4
Cancer Therapy

• Radiation has been an effective tool for
treating cancer for more than 100 years
• More than 60 percent of patients diag...

6
Advances in Radiation Oncology
Figure 1(b) of Lee et al. "Physical and radiobiological evaluation of radiotherapy treatm...

• Radiation Oncologist prescribes and oversees the radiation therapy treatments
• Medical Physicist ensures that treatment...

• Medical physics is easy to define
 Application of physics to medicine
• There is a long tradition of using applied phys...

9
AI in Radiation Oncology
Huynh et al. Nat Rev Clin Oncol 2020
A general overview of the radiation therapy workflow with ...

Diagnosis Simulation Contouring
Treatment
Planning
Treatment
QA
Treatment
Delivery
MIM Protégé AI &
Auto export to
Eclipse...

Radiomics
11
Aerts et al. Nature Communications, 2014
Figure 1: Extracting radiomics data from images. Figure 2: Analysis ...

• Quantitative approach to medical imaging
• Extracts many features from medical images
• These features can be used to pr...

A Novel
Functional
Radiomics
using Cardiac
FDG-PET/CT

• Traditional methods of evaluating cardiotoxicity:
 Focus solely on radiation doses to the heart.
 Do not incorporate f...

• Pre-Treatment only - before treatment
 Dose prediction
 Cardiac PET/CT Radiomics to predict existing heart disease
 C...

16
AI in Radiation Oncology – Heart Toxicity Prediction
Huynh et al. Nat Rev Clin Oncol 2020
A general overview of the rad...

Diagnostic PET/CT Radiation Target delineation Normal tissue delineation Radiation dose distribution
Diagnostic PET/CT Rad...

• The study included 209 pre-treatment 18F-FDG PET-CT images of lung cancer patients from 3
different study populations (T...

19
Method - Study Flow Diagram
Figure 1. Study and cardiac model diagram, training dataset: TJU (N=99), and external valid...

• Training and Validation Data:
 Training dataset: TJU (N=100), 80% training set (TPOT optimization), 20% validation set....

21
Results: Pipeline Optimization using TPOT
Feature Selector
Variance Threshold
Threshold: 0.0001
56 features
Threshold: ...

• TPOT (Tree-Based Pipeline Optimization Tool)
 100 populations, 10 generations, 10-fold CV
 Evaluated 10,000 pipeline c...

Results – Feature reduction and Prediction results
Model iteration and explanation
Number of
features
10-fold cross
valida...

• Delta-Radiomics calculation:
The change between pre- and post-PET/CT.
 Difference Δ𝐹𝑖
= 𝐹𝑝𝑜𝑠𝑡
𝑖
− 𝐹𝑝𝑟𝑒
𝑖
𝐹: the feature...

• Utilized dataset of 209 patients for functional cardiac radiomics model
development.
• Achieved high accuracy:
 9 optim...

Thank you !

Jefferson.edu

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